Service providers and device manufacturers (e.g., wireless, cellular, etc.) are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services. One area of development has been the use of recommendation systems to provide users with suggestions or recommendations for content, items, etc. available within the services and/or related applications (e.g., recommendations regarding people, places, or things of interest such as companions, restaurants, stores, vacations, movies, video on demand, books, songs, software, articles, news, images, etc.). For example, a typical recommendation system may suggest an item to a user based on a prediction that the user would be interested in the item—even if that user has never considered the item before—by comparing the user's preferences to one or more reference characteristics. Such recommendation systems historically have been based on collaborative filters that rely on often large amounts of user data (e.g., historical rating information, use history, etc.). However, such user data often is not available or has not been collected with respect to a particular service or application. Accordingly, service providers and device manufacturers face significant technical challenges to enabling development and generation of recommendation systems and models which are not based on having large sets of user data.